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Formation of Channel Correlation Based Significant Virtual Images from EEG Sub-bands Data to Recognize Human Emotion using Convolutional Neural Network
No full textThis thesis is submitted to the Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Electrical and Electronic Engineering, July 2019.Cataloged from PDF Version of Thesis.Includes bibliographical references (pages 85-91).Emotions are the most fundamental feature for non-verbal communication between human and machine. But the improvement of different human interacted media depends largely on the appropriate recognition of the human feeling that means what he or she wants just right at that moment. To communicate computer with human, to detect present feelings of the patient, to understand customer interest for shopping, to realize interest of physically handicapped people or to detect the lie of a convict; emotion recognition is a fundamental prerequisite. But due to some complexities, the proper recognition of human emotion from Electroencephalogram (EEG) has become too much challenging.
Normally, feature-based emotion recognition requires a strong effort to design the perfect feature or feature set related to the classification of emotion. To curtail the manual human effort of feature extraction, we designed a model with Convolutional Neural Network (CNN). As Electroencephalogram (EEG) is 1D data, to use CNN the 1D EEG data have to be converted into 2D significant image data. That is a challenging task. To meet up this challenge, initially, we calculated Pearson’s correlation coefficients form different sub-bands of EEG to formulate a virtual image. Later, this virtual image was fed into a CNN architecture to classify emotion. We made two distinct protocols; between these, protocol-1 was to classify positive and negative emotion and protocol-2 was to classify three distinct emotions. Overall maximum accuracy of 76.52% on valence and 76.82% on arousal was obtained by using internationally authorized DEAP dataset. We observed that the Convolutional Neural Network (CNN) based method showed state-of-the-art performance for emotion classification.Md. Hasibul HasanMaster of Science in Electrical and Electronic Engineerin
Solving Multi-Objective Assignment Problem with Decision Maker’s Preferences by Using Genetic Algorithm
No full textThis thesis is submitted to the Department of Mechanical Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering, June 2019.Cataloged from PDF Version of Thesis.Includes bibliographical references (pages 43-45).The multi-objective assignment problem is basically the N men –N tasks problem, where a single task has to be assigned to an individual with a view of optimizing the outcomes. A common challenge is to address the conflicting objectives which produce Pareto–optimal solutions. The main feature of the work is- normalizing all the criteria into a single scale regardless of their measurement units and their demand of minimum or maximum, which reliefs the researchers from careful attention in quantifying the quality criteria. The methodology also included the decision maker’s preferences regarding the objectives. While solving the problem through a genetic algorithm, a new encoding scheme is used together with a partially matched crossover (PMX). The working principle of the proposed algorithm is illustrated with a numerical example and its effectiveness has been compared with some well-established methodologies. It is found that the proposed algorithm provides a better solution with minimal computational effort.Md. Mahbubur RahmanMaster of Science in Mechanical Engineerin